{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 01 - Introduction To Causal Inference\n",
    "\n",
    "\n",
    "\n",
    "## What is Causal Inference\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why we Do Causal Inference\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning and Causal Inference\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Association and Causation\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-11T11:30:37.654067Z",
     "start_time": "2023-05-11T11:30:35.955633Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.special import expit\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from cycler import cycler\n",
    "\n",
    "\n",
    "\n",
    "default_cycler = (cycler(color=['0.3', '0.5', '0.7', '0.5']) +\n",
    "                  cycler(linestyle=['-', '--', ':', '-.']) + \n",
    "                  cycler(marker=['o', 'v', 'd', 'p']))\n",
    "\n",
    "color=['0.3', '0.5', '0.7', '0.5']\n",
    "linestyle=['-', '--', ':', '-.']\n",
    "marker=['o', 'v', 'd', 'p']\n",
    "\n",
    "plt.rc('axes', prop_cycle=default_cycler)\n",
    "plt.rc('font', size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-11T11:30:37.669266Z",
     "start_time": "2023-05-11T11:30:37.655602Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>weeks_to_xmas</th>\n",
       "      <th>avg_week_sales</th>\n",
       "      <th>is_on_sale</th>\n",
       "      <th>weekly_amount_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12.98</td>\n",
       "      <td>1</td>\n",
       "      <td>219.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12.98</td>\n",
       "      <td>1</td>\n",
       "      <td>184.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.98</td>\n",
       "      <td>1</td>\n",
       "      <td>145.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.98</td>\n",
       "      <td>0</td>\n",
       "      <td>102.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>19.92</td>\n",
       "      <td>0</td>\n",
       "      <td>103.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>19.92</td>\n",
       "      <td>0</td>\n",
       "      <td>53.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  weeks_to_xmas  avg_week_sales  is_on_sale  weekly_amount_sold\n",
       "0      1              3           12.98           1              219.60\n",
       "1      1              2           12.98           1              184.70\n",
       "2      1              1           12.98           1              145.75\n",
       "3      1              0           12.98           0              102.45\n",
       "4      2              3           19.92           0              103.22\n",
       "5      2              2           19.92           0               53.73"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data/xmas_sales.csv\")\n",
    "data.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Treatment and the Outcome\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Fundamental Problem of Causal Inference\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-11T11:30:37.754259Z",
     "start_time": "2023-05-11T11:30:37.670698Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(10,5))\n",
    "sns.boxplot(y=\"weekly_amount_sold\", x=\"is_on_sale\", data=data, ax=ax)\n",
    "\n",
    "ax.set_xlabel(\"is_on_sale\", fontsize = 20)\n",
    "ax.set_ylabel(\"weekly_amount_sold\", fontsize = 20)\n",
    "ax.tick_params(axis='both', which='major', labelsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Causal Models\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interventions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Individual Treatment Effect\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Potential Outcomes\n",
    " \n",
    "### Consistency and Stable Unit Treatment Values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Causal Quantities of Interest\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Causal Quantities: An Example\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-11T11:30:37.763490Z",
     "start_time": "2023-05-11T11:30:37.755809Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i</th>\n",
       "      <th>y0</th>\n",
       "      <th>y1</th>\n",
       "      <th>t</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>te</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>300</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>450</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>600</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>800</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   i   y0   y1  t  x    y   te\n",
       "0  1  200  220  0  0  200   20\n",
       "1  2  120  140  0  0  120   20\n",
       "2  3  300  400  0  1  300  100\n",
       "3  4  450  500  1  0  500   50\n",
       "4  5  600  600  1  0  600    0\n",
       "5  6  600  800  1  1  800  200"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dict(\n",
    "    i= [1,2,3,4,5,6],\n",
    "    y0=[200,120,300, 450,600,600],\n",
    "    y1=[220,140,400, 500,600,800],\n",
    "    t= [0,0,0,1,1,1],\n",
    "    x= [0,0,1,0,0,1],\n",
    ")).assign(\n",
    "    y = lambda d: (d[\"t\"]*d[\"y1\"] + (1-d[\"t\"])*d[\"y0\"]).astype(int),\n",
    "    te=lambda d: d[\"y1\"] - d[\"y0\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-11T11:30:37.775180Z",
     "start_time": "2023-05-11T11:30:37.766202Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i</th>\n",
       "      <th>y0</th>\n",
       "      <th>y1</th>\n",
       "      <th>t</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>te</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   i     y0     y1  t  x    y  te\n",
       "0  1  200.0    NaN  0  0  200 NaN\n",
       "1  2  120.0    NaN  0  0  120 NaN\n",
       "2  3  300.0    NaN  0  1  300 NaN\n",
       "3  4    NaN  500.0  1  0  500 NaN\n",
       "4  5    NaN  600.0  1  0  600 NaN\n",
       "5  6    NaN  800.0  1  1  800 NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dict(\n",
    "    i= [1,2,3,4,5,6],\n",
    "    y0=[200,120,300, np.nan, np.nan, np.nan,],\n",
    "    y1=[np.nan, np.nan, np.nan, 500,600,800],\n",
    "    t= [0,0,0,1,1,1],\n",
    "    x= [0,0,1,0,0,1],\n",
    ")).assign(\n",
    "    y = lambda d: np.where(d[\"t\"]==1, d[\"y1\"], d[\"y0\"]).astype(int),\n",
    "    te=lambda d: d[\"y1\"] - d[\"y0\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bias\n",
    " \n",
    "### The Bias Equation \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "### A Visual Guide to Bias\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-11T11:30:37.915221Z",
     "start_time": "2023-05-11T11:30:37.776499Z"
    },
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7feb1c211610>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font', size=20)\n",
    "fig = plt.figure()    \n",
    "\n",
    "sns.lmplot(data=data,\n",
    "           ci=None,\n",
    "           x=\"avg_week_sales\",\n",
    "           y=\"weekly_amount_sold\",\n",
    "           scatter=False,\n",
    "           height=4, aspect=2)\n",
    "\n",
    "plt.scatter(x=data.query(\"is_on_sale==1\")[\"avg_week_sales\"],\n",
    "            y=data.query(\"is_on_sale==1\")[\"weekly_amount_sold\"],\n",
    "            label=\"on sale\",\n",
    "            color=color[0], alpha=.8, marker=marker[0])\n",
    "\n",
    "plt.scatter(x=data.query(\"is_on_sale==0\")[\"avg_week_sales\"],\n",
    "            y=data.query(\"is_on_sale==0\")[\"weekly_amount_sold\"],\n",
    "            label=\"not on sale\",\n",
    "            color=color[2], alpha=.6, marker=marker[1])\n",
    "plt.legend(fontsize=\"14\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying the Treatment Effect\n",
    " \n",
    "### The Independence Assumption\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identification with Randomization \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Ideas\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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